Browsing by Subject "reinforcement learning"

Now showing items 1-8 of 8

  • Importance Sampling for Reinforcement Learning with Multiple Objectives 

    Unknown author (2001-08-01)
    This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find ...

  • Mobilized ad-hoc networks: A reinforcement learning approach 

    Unknown author (2003-12-04)
    Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. ...

  • Mobilized ad-hoc networks: A reinforcement learning approach 

    Unknown author (2003-12-04)
    Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. ...

  • Modeling Stock Order Flows and Learning Market-Making from Data 

    Unknown author (2002-06-01)
    Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling ...

  • On the Convergence of Stochastic Iterative Dynamic Programming Algorithms 

    Unknown author (1993-08-01)
    Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton ...

  • Reinforcement Learning by Policy Search 

    Unknown author (2003-02-14)
    One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially ...

  • A Reinforcement-Learning Approach to Power Management 

    Unknown author (2002-05-01)
    We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our ...

  • Towards Feature Selection In Actor-Critic Algorithms 

    Unknown author (2007-11-01)
    Choosing features for the critic in actor-critic algorithms with function approximation is known to be a challenge. Too few critic features can lead to degeneracy of the actor gradient, and too many features may lead to ...